Infrared remote sensing of trace gases in the lower atmosphere from a satellite or aircraft platform is an extremely difficult task because the instrument must view the surface of the Earth. To maximise the energy gathered and to minimise noise due to scattering losses and clouds in the atmosphere, a nadir (or near-nadir) viewing geometry is required. Also, since the trace gas near the surface and the ground are approximately the same temperature, there is very little radiative contrast between the thermal emission of the gas and the surface at wavelengths longer than 3.5 μm. As a result, measurements must, in this embodiment, be made at shorter wavelengths, where reflected solar radiation becomes a significant component in the upwelling radiance. Consequently, any short-scale spatial or temporal variations in the surface, the atmosphere and/or the environment add noise into the measurement as the instrument passes over the surface.
The primary complications in such remote sensing measurements are spatial and temporal variations in the surface, the atmosphere and the environment. Any variations as the instrument passes over the surface has the potential to add noise and/or error to the measurement. The main sources of spatial and temporal variations include, variations in {a} surface reflectivity and emissivity, {b} surface temperature, {c} angle of the surface (relative to the nadir), {d} solar zenith angle (SZA, the angle between the Sun and the vertical), {e} scattering properties of the surface, and {f} absorption from gases in the atmosphere. The following section discusses each of these sources of error.
Reflectivity and emissivity—Reflectivity and emissivity are related by Kirchoff's Law, which states that the emissivity (ελ) of a surface equals absorptivity (Aλ), and equals one minus the reflectivity (Rλ).Kirchoff's law: ελ=Aλ=1−Rλ
FIG. 1A through 1E shows the total hemispheric reflectivity of many natural and man-made surfaces in the infrared (2-4 μm, 2500-5000 cm−1), obtained from the Jet Propulsion Laboratory, NASA, Aster Spectral Library. FIG. 1A shows the reflectivity of vegetation, FIG. 1B shows the reflectivity of certain types of rocks, FIG. 1C shows the reflectivity of certain types of soils, FIG. 1D shows the reflectivity of certain man-made materials, and FIG. 1E shows the reflectivity of snow, ice and water. These figures show large variations in the infrared reflectivity for all materials and between different materials. As a result, the upwelling radiance from the surface is strongly affected by the spatial distribution of surface types in any remote sensing instrument's field-of-view (FOV).
The emission of the surface and gases in atmosphere—The emission of the surface and gases in atmosphere are a function of their temperatures and their emissivities. In the infrared, most surfaces have a significant emissivity. Also, the higher the temperature, the larger the emission from the surface and atmosphere. In general, this is only a significant issue at wavelengths >3 μm, when thermal emission from the surface and atmosphere become a significant component in the background radiation field.
Reflected solar energy—The amount of solar energy reflected from a surface is dependent on the flux of solar energy incident on the surface. As the SZA increases, the incident energy per unit area (which is reflected) decreases, due to geometry and absorption by the atmosphere. This is also effected by the angle of the surface (ie. the topography) relative to the Sun.
Scattering properties of a surface—The scattering (reflective) properties of the surface greatly affect the upwelling reflected solar radiance. Energy which is scattered from the surface may be reflected in all directions. Some surfaces, such as oceans, are highly specular (minor-like) reflectors. Other surfaces, such as vegetation, are highly lambertian (isotropic) reflectors. In general, most surfaces are somewhere between specular and lambertian. FIG. 2A through 2D shows various types of reflectors from an ideal specular reflector in FIG. 2A to an ideal lambertian reflector in FIG. 2D. As a consequence, upwelling radiance varies with the scattering properties of the surface materials.
Distribution of concentration of gases—The spatial and temporal distribution of the concentration of gases that absorb energy within the passband of the remote sensing measurement may vary spatially, resulting in variations in upwelling radiance.
The upwelling radiance from the Earth's surface varies spatially, temporally and spectrally. In general these variations occur over all spatial scales in which the atmosphere is remotely sensed. Any remote sensing technique that observes the surface will require a methodology to remove or reduce the noise induced by these variations.
Gas-Filter Correlation Radiometry
One remote sensing technique that has been utilized for remote sensing of trace gases near the surface is known as Gas-Filter Correlation Radiometry (GFCR). A GFCR is a remote sensing radiometer that uses a sample of the gas as a spectral filter, providing enhanced sensitivity and selectivity to that gas. FIG. 3 is a schematic showing the principles of GFCR. Incoming radiation 12 is passed through a gas cell 14, known as a correlation cell, which is undergoing a gas-density modulation (molecules per cm2) along its optical path (the gas in the correlation cell is the same as the gas being remotely sensed). The radiation is then passed through a narrow bandpass filter 16, which passes only a narrow spectral range selected to cover an absorption band of the gas of interest. The radiation is then measured by an infrared detector 18. In other embodiments, the type of detector will depend on the radiation being detected. The modulation of the gas-density in the correlation cell produces a modulation in detector signal, which is measured using a phase-sensitive detector 20. This signal corresponds to the energy entering the radiometer at wavelengths corresponding to the absorption lines in the passband of the gas of interest.
In operation, gas density modulation has been performed by a number of techniques. A Selective Chopper radiometer (SCR) modulates gas-density by “chopping” between two correlation cells of different gas pressure, and sometimes length. A Pressure-Modulator Radiometer (PMR) modulates the pressure of the gas inside a static correlation cell. A Length Modulated Radiometer (LMR) modulates the path length of gas inside the correlation cell by rotating a bow-tie shaped rotor of an inert optical material inside the cell. The most recent satellite instrument to attempt to measure lower atmospheric trace gases using GFCRs was the MOPITT (Measurements Of Pollution In The Troposphere) instrument launched on NASA's Terra satellite in December 1999.
FIG. 4 is a schematic diagram detailing a recent fourth configuration of a GFCR, known as a Simultaneous-View Correlation Radiometer (SVCR). In this configuration, the incoming energy is split by a beam splitter 22 onto two optical paths 24 and 26 with two gas cells 28 and 30 of different gas density, and two infrared detectors 32 and 34. Gas cell 28 may be evacuated. It may also be filled with an optically inert gas. Gas cell 30 may be referred to as a correlation cell. Instead of a sequential gas-density modulation in detector signals, the GFCR measurement is performed by comparing the simultaneous signals of the two detectors using a computer 36.
FIG. 5 illustrates how a GFCR 38 detects the presence of the trace gas in its FOV. The two channels 44 and 46 of the radiometer detect two signals, S1 and S2 respectively. If a cloud of absorbing gas 40 (the same gas as in the correlation cell 30) is in the FOV 42 of the GFCR 38, the signal in the first channel 44 is reduced due to the absorption of this gas (S1−ε). However, since the second channel 46 already has the energy absorbed by the gas in the correlation cell 30 (at the wavelengths corresponding to the absorption lines of the gas), the signal does not change (at least to first order).
In operation, two GFCR signals are defined, a difference signal (Sdiff) and an average signal (Savg). Sdiff is the difference between S1 and S2, and Savg is the average of S1 and S2. Sdiff is primarily a measurement of the upwelling radiance at the wavelengths corresponding to the absorption lines of the gas of interest. Savg is primarily a measure of the upwelling radiance across the entire passband. A third GFCR signal, known as the “instrument signal”, is often also defined, the “difference-to-average ratio” (Sdiff/Savg). Defining the instrument signal in this matter is convenient as the value is unitless, and the effects of any “grey” absorption or scattering features (ie. constant or uniform over the radiometer passband) in the atmosphere, environment or the instrument are removed. This includes any uniform variation in the surface reflectivity.
For GFCR measurements of the lower atmosphere, Sdiff/Savg is commonly used because it greatly reduces the effects of the generally unknown and highly variable surface reflectivity. This strategy does greatly reduce the errors induced by variations in the upwelling source radiance. However, the short wavelength “solar” channels of MOPITT which were to measure the total CO (2.34 μm) and CH4 (2.26 μm) column concentrations were noisy and did not achieve their designed accuracy. These channels worked best over the oceans, where the spatial surface variations were minimal.
Surface Reflectivity
Until recently, it has been assumed that the primary cause for the failure of the MOPITT solar channels was that the LMRs used in these channels made sequential measurements of the gas density states. The rotors in the MOPITT LMRs modulated the gas path length at a rate of 40 Hz. In the time for the LMR to go from one gas-density state to the other, the FOV of the instrument (nominally 20×20 km) moved approximately 175 m across the surface. As such, any spatial variations in the upwelling radiance was convolved into instrument signals, creating noise. This effect was confirmed by observing large peaks in measurement noise when the FOV of MOPITT passed over a major transition in surfaces, such as from land (high reflectivity) to the ocean (low reflectivity). This issue can be relieved by changing the type of GFCR used, from the LMR to a form which makes simultaneous measurements of the two gas-density states (such as the SVCR).
However, a second problem relating to surface variations has recently been identified for surface-viewing remote sensing GFCR measurements. As stated previously, by defining the instrument signal as the ratio of the difference-to-average signals (Sdiff/Savg), any “grey” or uniform variation (over the passband) in the surface reflectivity/emissivity is removed. However, it is rarely the case that variations in the reflectivity over the passband are uniform. Instead, as the GFCR moves over different surface types, the upwelling radiance from different sections of the instrument passband varies. This results in a change in the “weighting” of different sections of the passband in the GFCR signals. Since the absorption lines of the gas of interest are neither uniformly nor randomly distributed over the passband (either in strength or in position), the instrument signal (Sdiff/Savg) varies with the surface type. For example, if a change in the surface reflectance increases upwelling radiation in a region of strong absorption of the measured gas, this will result in an increase Sdiff but have a smaller relative increase on Savg. Similarly, if a change in the surface reflectance increases upwelling radiation in a region of weak(er) absorption by the measured gas, this will result in an increase Savg but have a much smaller relative increase on Sdiff. Consequently, variations in the reflectivity of the surface over the passband add noise/error into the GFCR instrument signal (Sdiff/Savg).
As an example, FIGS. 6 and 7 show the passband of one of the MOPITT solar CO (channel #2) and CH4 (channel #4) channels, including the bandpass filter transmission profiles of the channels, the transmission of the long path density state of the correlation cell of the LMR, and the measured total hemispheric reflectance of dry grass, conifer needles and water. As can be seen the distribution of CH4 and CO absorption lines within the passbands are neither random nor uniform. For the CH4 case, stronger absorption occurs on the lower wavenumber (higher wavelength) side of the passband. If the surface reflectivity change uniformly over the passband (ie. a “grey” or uniform variation), then the net effect on the Sdiff/Savg signal will be zero (ie. the Sdiff and Savg signals change by the same fractional amount). However, if the surface reflectivity changes by different amounts over the passband (as highlighted by the differences in the three reflectivity curves), this will cause an offset (ie. error) in the Sdiff/Savg signal.
To further highlight the potential impact of this surface reflectance effect, FIG. 8 shows the passband of the RealSens™ GFCR instrument for detection of ethane (C2H6), plus the total hemispheric reflectance curves for seven different surface types. RealSens™ is a recent commercial aircraft-based GFCR instrument for remotely sensing the presence of leaked natural gas. This figure shows that for the RealSens™ measurement, surface reflectivity is an important factor in data retrieval, more so than for the MOPITT solar channels (although it is still an important and limiting problem for the MOPITT GFCRs).
It should be noted, of all the sources of variance in upwelling radiation listed above, only reflectivity (and associated emissivity) and absorption by gases in the atmosphere produce non-uniform variations over the passband in the upwelling radiation. All other factors, including SZA, surface angle, and scattering induce, to first order, introduce only uniform variations (over the passband) in the upwelling radiation. Therefore, surface reflectivity is a primary source of short spatial scale noise in nadir-viewing GFCR measurements.